The original idea for the fund was synthesized in a
classroom at UC-Berkeley, where founders Chida Khatua and Art
Amador met during an entrepreneurship class.

Khatua's background in AI and machine learning
complemented Amador's history in private wealth management, and
the duo decided to launch an exchange-traded fund.

When Art Amador worked in private wealth management at Fidelity,
his clients expected him to know absolutely everything.

Whether it related to global markets, macroeconomic factors,
specific companies, or full sectors, their curiosities were wide
ranging — and Amador wondered if he'd ever find a way to be the
all-knowing oracle they desired.

That all changed one day in the fall of 2014 when Amador was
pursuing his MBA at the Haas School of
Business at the University of California at Berkeley.

As part of an entrepreneurship class, he was placed in the same
cohort as a long-time Intel engineer and machine-learning
specialist named Chida Khatua, and the two got to talking. That
conversation led to what its creators say is the world's first
AI-powered exchange-traded fund, one built on technology that
could change the paradigm for how computers are used to invest.

The fund — powered by IBM's Watson supercomputing technology —
didn't end up launching for a few more years, but its roots can
be traced back to that fateful first conversation at Berkeley.

"I was telling him it was impossible to have infinite knowledge
about every stock, and about everything going on in markets," he
tells Business Insider. "I told him that there's simply too much
information out there and not enough time to distill it into
actionable ideas."

As it turned out, Khatua had been researching for years how to
sift through massive amounts of data in a way that extended far
beyond human capabilities. With two master's degrees in computer
engineering — including one from Stanford — he worked at Intel
for 18 years, mostly focusing on machine learning.

"His background — in artificial intelligence and machine learning
— was the perfect use case," Amador says. "We started talking
about how that could apply to the equity markets."

Birth of an ETF

Even though the early groundwork had been laid for what would
eventually become their newest venture, Khatua and Amador went
their separate ways after the program ended. But the gears in
Khatua's head never stopped turning, and in September 2016 he
invited Amador to join him in building a product that would
combine their respective areas of expertise.

Amador took some time to think about it. In his mind, the result
would be an AI-powered quantitative hedge fund, and he wasn't
sure if he wanted to give up his job at Fidelity for that. But
Khatua had other ideas: He wanted to build and launch an ETF.

To him, the ideal application for his technology was to get it
into as many hands as possible. And if he combined it with
Amador's investment prowess, they could build an ETF available to
be traded by the average person with a brokerage account.

"Working at Intel gave me insight into how machine learnings and
AI technology is maturing and how the benefits it offers can
really be maximized," Khatua tells Business Insider. "It
gave me a unique perspective, and I asked myself for a while when
the right time would be to go out and create some product that
can help many people."

Acting like a rational investor

A big part of Amador's decision to ultimately join Khatua in
pursuing an ETF was the latter's acceptance into the highest tier
of the IBM Global Entrepreneurship
Program. After all, his machine learning and AI efforts were
powered by the company's Watson supercomputer.

That gave Khatua $125,000 with which to pursue his idea, and it
provided Amador crucial validation for the endeavor. He joined up
shortly thereafter, and the duo launched Equbot.

Then they put Watson to work. The eventual result was the
recently launched AI Powered Equity
ETF (ticker: AIEQ), which analyzes more data than humanly
possible, all in the pursuit of building the perfect portfolio of 30
to 70 stocks. And the technology enables it to do that while
constantly analyzing information for 6,000 US-listed companies.

But there's a wrinkle. Equbot's AI model is built to act like a
rational investor. In addition to analyzing regulatory filings,
quarterly news releases, articles, social-media postings, and
management teams, it's also designed to assess market sentiment
and weed out potentially faulty inputs — including so-called fake
news.

"A rational investor looks at a company as a whole and they draw
insight into what’s right looking at the complete picture,"
Khatua says. "The AI model helps us do that. The technology
doesn’t only help you decide what to do; it can also educate you
on why it’s happening."

The technology doesn’t only help you decide what to do; it can
also educate you on why it’s happening.

That's a key element of AIEQ and one that sets it apart from the
hedge funds that use AI to construct trading strategies. Khatua
says many of those models function as a "conceptual black box,"
because the presence of certain stocks can't be explained in a
rational way. In his mind, Equbot's ETF offers the best of both
worlds: It's based on a mountain of analysis and the
stock-picking methodology can be explained.

"We know why something's in our portfolio after our system
chooses it," Amador says. "'The system picked it' is not usually
an explanation that investors will buy."

Further, the machine-learning aspect of AIEQ is crucial in
avoiding human error. Amador points out that even if a firm had
6,000 analysts each responsible for reading 150 to 200 articles
about one stock each day, that work would have to be
cross-referenced against the findings of all other employees,
then funneled into one objective opinion.

"Humans don’t have the speed, capacity, or retention to do this,"
he says.

But it's far too early to judge the success of AIEQ based on five
weeks of returns. The more telling statistic is the volume of
shares traded. The ETF has seen an average of 259,000 units
change hands daily, a strong showing for a fledgling fund. It had
about $70 million in assets on Monday, roughly 10 times its size
during the first week of trading.

The way that Khatua and Amador see it, interest in their product
will continue to grow as long as personal bias continues to cloud
investment decisions — something they see happening even at the
highest level of professional money management.

"You can remove that by making this investment process more
autonomous, as we've done," Amador says. "It's nothing against
people. It's just human instinct."